{"id":"W2351534238","doi":"","title":"The structures of Turbo-Equalization and Iterative Equalization","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Industrial Technology and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Turbo; Equalization (audio); Turbo equalizer; Algorithm; Decoding methods; Error floor; Low-density parity-check code","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004932056,0.00006758863,0.00008488193,0.00003868697,0.0002055221,0.00001384588,0.00009873239,0.00008282228,0.000001658538],"category_scores_gemma":[8.177003e-7,0.00005272108,0.00001650751,0.0001469265,0.00007551546,0.00004160934,0.00001680988,0.00006616279,0.000002923517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001198268,"about_ca_system_score_gemma":0.000007256752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004443748,"about_ca_topic_score_gemma":0.000003363004,"domain_scores_codex":[0.9995911,0.00001952108,0.0001837928,0.00008279034,0.0000427931,0.00008007276],"domain_scores_gemma":[0.9997262,0.0000525486,0.00004255597,0.0001184368,0.00004655822,0.00001370136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001729636,0.00003997652,0.0017965,0.00009502789,0.000229358,0.000001065679,0.003995413,0.01176525,0.138757,0.498226,0.006752351,0.3383248],"study_design_scores_gemma":[0.001775559,0.00008201299,0.007125205,0.00004581947,0.00005878146,0.0001290651,0.0002756011,0.04849544,0.1986159,0.03173482,0.7110271,0.0006346924],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05668928,0.001017426,0.940755,0.00009441397,0.00004164929,0.0006122236,0.00001138365,0.0002106247,0.0005680117],"genre_scores_gemma":[0.9974286,0.00009136138,0.002183427,0.00001864524,0.00009325697,0.0001187803,0.00001618761,0.000008790043,0.00004097252],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9407393,"threshold_uncertainty_score":0.2149903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009119747579980129,"score_gpt":0.1994572019189897,"score_spread":0.1903374543390096,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}